One group is the model optimization to quickly fit the distribution of new categories. Typical methods attempt to update the model parameters or optimizer for this purpose. For instance, meta-learner long short-term memory (LSTM) [1]} can update the model parameters to initialize the classifier network for the quick training convergence in the few samples of each classes; Model-agnostic meta-learning (MAML) [2]} can train the small gradient updating based on few learning data from a new task to obtain the well generalization performance; Latent embedding optimization(LEO)[3]}
can learn the latent generative representation of model parameters based on data dependence to decouple the gradient adaptation from the high-dimension parameters space.
